The Dempster-Shafer theory has been extended recently for its application to expert systems. However, implementing the extended D-S reasoning model in rule-based systems greatly complicates the task of generating informative explanations. By implementing GERTIS, a prototype system for diagnosing rheumatoid arthritis, we show that two kinds of knowledge are essential for explanation generation: (l) taxonomic class relationships between hypotheses and (2) pointers to the rules that significantly contribute to belief in the hypothesis. As a result, the knowledge represented in GERTIS is richer and more complex than that of conventional rule-based systems. GERTIS not only demonstrates the feasibility of rule-based evidential-reasoning systems, but also suggests ways to generate better explanations, and to explicitly represent various useful relationships among hypotheses and rules.

A major difficulty in developing and maintaining very large knowledge bases originates from the variety of forms in which knowledge is made available to the KB builder. The objective of this research is to bring together two complementary knowledge representation schemes: term subsumption languages, which represent and reason about defining characteristics of concepts, and proximate reasoning models, which deal with uncertain knowledge and data in expert systems. Previous works in this area have primarily focused on probabilistic inheritance. In this paper, we address two other important issues regarding the integration of term subsumption-based systems and approximate reasoning models. First, we outline a general architecture that specifies the interactions between the deductive reasoner of a term subsumption system and an approximate reasoner. Second, we generalize the semantics of terminological language so that terminological knowledge can be used to make plausible inferences. The architecture, combined with the generalized semantics, forms the foundation of a synergistic tight integration of term subsumption systems and approximate reasoning models.

Dempster's rule of combination has been the most controversial part of the Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on the noncombinability of evidence from a relational model of the D-S theory. In this paper, we will describe another relational model where D-S masses are represented as conditional granular distributions. By comparing it with Zadeh's relational model, we will show how Zadeh's conjecture on combinability does not affect the applicability of Dempster's rule in our model.

The Association for the Advancement of Artificial Intelligence presented its 2005 Spring Symposium Series on Monday through Wednesday, March 21-23, 2005 at Stanford University in Stanford, California. The topics of the eight symposia in this symposium series were (1) AI Technologies for Homeland Security; (2) Challenges to Decision Support in a Changing World; (3) Developmental Robotics; (4) Dialogical Robots: Verbal Interaction with Embodied Agents and Situated Devices; (5) Knowledge Collection from Volunteer Contributors; (6) Metacognition in Computation; (7) Persistent Assistants: Living and Working with AI; and (8) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance.

Techniques in this symposium series were he calls the "twenty-first century for analyzing terrorist networks (1) AI Technologies for Homeland Security; strategic threat triad," which consists were reported by Alphatech (2) Challenges to Decision of failed states, global terrorism, and and the University of Arizona. Popp noted that and retrieving information for Robots: Verbal Interaction with convergence of these three elements counter intelligence was demonstrated Embodied Agents and Situated Devices; is highly destabilizing and a key by Jim Hendler of the University (5) Knowledge Collection from strategic concern to the national security of Maryland. They also aimed to chart out future from Stanford University, Lawrence For example, systems that are research agenda by identifying specific Livermore Laboratories, SRI International, based on probabilistic or decisiontheoretic interesting issues in various and Syracuse University. Homeland security applications for unable to cope with change by themselves, The recurrent themes from data mining and mobile robots were as neither probability theory the presentations included the following: reported by Alphatech and the University nor decision theory says much about of South Florida, respectively. How do The highlights of the symposium let alone how they should be modified.

Term Subsumption Languages in Knowledge Representation

Jim when we want to define the class of should be justified by something Schmolze argued that if you think of people who work in specific institutions), other than the code implementing a sort of lingua franca for knowledge (2) when a concept definition the system. However, interpreting the representation, you can't be committed depends on the assertional properties two terms efficient and principled as to the difference between terminological of its instances (as with gray elephants, worst-case tractability and soundness and assertional knowledge for example), and (3) when and completeness with respect to the or even between roles and concepts.